Local Semantic Search Engine (Mini-RAG)

Built a local semantic search engine using a mini-RAG (Retrieval-Augmented Generation) architecture in Python. The tool allows users to search through internal documents using natural language queries instead of exact keyword matches.

Document Ingestion: The system processes and indexes documents locally, converting text into vector embeddings that capture semantic meaning.

Natural Language Search: Users type questions or descriptions in plain language. The engine retrieves the most relevant document sections based on meaning, not just keyword overlap.

Local & Private: Everything runs locally. No data leaves the machine, making it suitable for sensitive internal documents, legal files, or proprietary content.

Lightweight Architecture: Built as a minimal RAG pipeline without heavy infrastructure requirements. Can run on a standard development machine or a small server.

Internal knowledge bases and documentation search

Legal document retrieval

Research paper discovery

Customer support knowledge management

A fast, private, and accurate semantic search tool that makes internal document retrieval feel like asking a question to someone who has read everything.